Search

Aim To describe a research protocol designed to formulate a conceptual framework of informal caregiver resilience in palliative care. Background Resilience is the ability to adapt or to improve one's own conditions following experiences of adversity. The end-of-life care provided by informal caregivers is a form of adversity because it entails objective difficulties, emotional involvement and deep levels of introspection that have been stimulated by the death event. Resilience has not yet been addressed in association with end-of-life care. Design This is a multicentre cross-sectional study. Methods We will administer a questionnaire to a sample of informal end-of-life caregivers to collect data about the main psychological, behavioural and healthcare factors that impact resilience. Data analysis will include descriptive and correlational statistical techniques, multiple linear regressions and structural equation modelling. Data will be collected in multiple palliative care centres and statistical analysis will be carried out using software: SPSS version 19.0 and MPlus version 7.3. The study is supported by a grant from the Centre of Excellence for Nursing Scholarship in Italy (Research Grant number 2.13.10) that was awarded in March 2013. Discussion The study seeks to identify the predictive, mediating and moderating roles of select variables: caregivers' self-efficacy, burdens of caregiving, depression and resilience. The results of this analysis will impact the theoretical study of resilience in palliative care and will have practical implications for interventions aimed at supporting caregivers through healthcare teams. [ABSTRACT FROM AUTHOR]

Aims To describe the utility of artificial neural networks in predicting communication risks. Background In health care, effective communication reduces the risk of error. Therefore, it is important to identify the predictive factors of effective communication. Non-technical skills are needed to achieve effective communication. This study explores how artificial neural networks can be applied to predict the risk of communication failures in emergency departments. Design A multicentre observational study. Methods Data were collected between March-May 2011 by observing the communication interactions of 840 nurses with their patients during their routine activities in emergency departments. The tools used for our observation were a questionnaire to collect personal and descriptive data, level of training and experience and Guilbert's observation grid, applying the Situation-Background-Assessment-Recommendation technique to communication in emergency departments. Results A total of 840 observations were made on the nurses working in the emergency departments. Based on Guilbert's observation grid, the output variables is likely to influence the risk of communication failure were 'terminology'; 'listening'; 'attention' and 'clarity', whereas nurses' personal characteristics were used as input variables in the artificial neural network model. A model based on the multilayer perceptron topology was developed and trained. The receiver operator characteristic analysis confirmed that the artificial neural network model correctly predicted the performance of more than 80% of the communication failures. Conclusion The application of the artificial neural network model could offer a valid tool to forecast and prevent harmful communication errors in the emergency department. [ABSTRACT FROM AUTHOR]

Aim To test the situation-specific theory of heart failure self-care with structural equation modelling. Background Several authors have proposed theories on heart failure self-care, but only the situation-specific theory of heart failure self-care by Riegel and Dickson is focused on the process that patients use to perform self-care. This theory has never been tested with structural equation modelling. Design A secondary analysis of data from a cross-sectional study. Methods Patients with heart failure were recruited in 21 cardiovascular centres across Italy during 2011. Data were collected with a sociodemographic questionnaire, chart abstraction for clinical data and the Self- Care of Heart Failure Index v.6·2. Results A sample of 417 participants was enrolled in the study (59% males, mean age 72 years). The following propositions were tested and supported: Symptom monitoring correlates with treatment adherence; symptom monitoring and treatment adherence have a direct, positive relationship with symptom recognition and evaluation that in turn have a direct, positive relationship with treatment implementation; treatment implementation has a direct, positive relationship with treatment evaluation. In addition, the following three relationships were found: Symptom monitoring has a direct, positive relationship with treatment implementation; symptom recognition and evaluation have direct, positive relationships with treatment evaluation and symptom monitoring correlates with treatment evaluation. [Correction added on 9th April 2013, after first online publication: '...symptom monitoring correlates with treatment implementation.' has been corrected to read '...symptom monitoring correlates with treatment evaluation.'] Conclusion The data support the situation-specific theory of heart failure self-care with the addition of three new relationships that emerged from the analysis. Results of this study lend further support to the use of the situation-specific theory of heart failure self-care in research and practice. [ABSTRACT FROM AUTHOR]